# -*- coding: utf-8 -*-
# __init__.py create by kumiler
# on 2022/11/28 19:11
# desc
# from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from utils.operators.cluster_for_spark_sql_operator import SparkSqlOperator
from datetime import timedelta
from jms.ods.others.tab_direct_rule_config import jms_ods__tab_direct_rule_config

jms_dim__dim_tab_direct_rule_config_base = SparkSqlOperator(
    task_id='jms_dim__dim_tab_direct_rule_config_base',
    task_concurrency=1,
    pool_slots=2,
    master='yarn',
    name='jms_dim__dim_tab_direct_rule_config_base_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dim/dim_tab_direct_rule_config_base/execute.sql',
    executor_cores=2,
    executor_memory='2G',
    email=['rabie.zhuang@jtexpress.com', 'yl_bigdata@yl-scm.com'],
    num_executors=2,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled': 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors': 3,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode': 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead': '1G',  # 堆外内存
          'spark.sql.shuffle.partitions': 10,
          },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=10),
)
jms_dim__dim_tab_direct_rule_config_base << jms_ods__tab_direct_rule_config